Python Code Performance of Machine Learning Models in Seasons
Published: 20 August 2024| Version 2 | DOI: 10.17632/78mztpjnf6.2
Contributor:
Andrew ToluTaiwoDescription
These codes implements machine learning models such as Multilinear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) with geospatial and non-geospatial data to predict volume of water consumed by poor urban households in dry and wet seasons
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Categories
Machine Learning, Geoinformatics, Water for Poverty Reduction